scholarly journals Can assimilation of satellite observations improve subsurface biological properties in a numerical model? A case study for the Gulf of Mexico

Ocean Science ◽  
2021 ◽  
Vol 17 (4) ◽  
pp. 1141-1156
Author(s):  
Bin Wang ◽  
Katja Fennel ◽  
Liuqian Yu
Keyword(s):  
Data Assimilation ◽  
Gulf Of Mexico ◽  
A Priori ◽  
Three Dimensional ◽  
Biological Properties ◽  
Satellite Observations ◽  
Sea Surface ◽  
Biogeochemical Models ◽  
Optical Module ◽  
Argo Profiles

Abstract. Given current threats to ocean ecosystem health, there is a growing demand for accurate biogeochemical hindcasts, nowcasts, and predictions. Provision of such products requires data assimilation, i.e., a comprehensive strategy for incorporating observations into biogeochemical models, but current data streams of biogeochemical observations are generally considered insufficient for the operational provision of such products. This study investigates to what degree the assimilation of satellite observations in combination with a priori model calibration by sparse BGC-Argo profiles can improve subsurface biogeochemical properties. The multivariate deterministic ensemble Kalman filter (DEnKF) has been implemented to assimilate physical and biological observations into a three-dimensional coupled physical–biogeochemical model, the biogeochemical component of which has been calibrated by BGC-Argo float data for the Gulf of Mexico. Specifically, observations of sea surface height, sea surface temperature, and surface chlorophyll were assimilated, and profiles of both physical and biological variables were updated based on the surface information. We assessed whether this leads to improved subsurface distributions, especially of biological properties, using observations from five BGC-Argo floats that were not assimilated. An alternative light parameterization that was tuned a priori using BGC-Argo observations was also applied to test the sensitivity of data assimilation impact on subsurface biological properties. Results show that assimilation of the satellite data improves model representation of major circulation features, which translate into improved three-dimensional distributions of temperature and salinity. The multivariate assimilation also improves the agreement of subsurface nitrate through its tight correlation with temperature, but the improvements in subsurface chlorophyll were modest initially due to suboptimal choices of the model's optical module. Repeating the assimilation run by using the alternative light parameterization greatly improved the subsurface distribution of chlorophyll. Therefore, even sparse BGC-Argo observations can provide substantial benefits for biogeochemical prediction by enabling a priori model tuning. Given that, so far, the abundance of BGC-Argo profiles in the Gulf of Mexico and elsewhere has been insufficient for sequential assimilation, updating 3D biological properties in a model that has been well calibrated is an intermediate step toward full assimilation of the new data types.

scholarly journals Can assimilation of satellite observations improve subsurface biological properties in a numerical model? A case study for the Gulf of Mexico

2021 ◽  
Author(s):  
Bin Wang ◽  
Katja Fennel ◽  
Liuqian Yu
Keyword(s):  
Gulf Of Mexico ◽  
A Priori ◽  
Biological Properties ◽  
Satellite Observations ◽  
Sea Surface ◽  
Biogeochemical Model ◽  
Biological Variables ◽  
Biogeochemical Models ◽  
Optical Module ◽  
Argo Profiles

Abstract. Given current threats to ocean ecosystem health, there is a growing demand for accurate biogeochemical hindcasts, nowcasts, and predictions. Provision of such products requires data assimilation, i.e., a comprehensive strategy for incorporating observations into biogeochemical models, but current data streams of biogeochemical observations are generally considered insufficient for the operational provision of such products. This study investigates to what degree the satellite observations in combination with sparse BGC Argo profiles can improve subsurface biogeochemical properties. The multivariate Deterministic Ensemble Kalman Filter (DEnKF) has been implemented to assimilate physical and biological observations into a biogeochemical model of the Gulf of Mexico. First, the biogeochemical model component was tuned using BGC-Argo observations. Then, observations of sea surface height, sea surface temperature, and surface chlorophyll were assimilated, and profiles of both physical and biological variables were updated based on the surface information. We assessed whether this leads to improved subsurface distributions, especially of biological properties, using observations from five BGC-Argo floats that were not assimilated, but used in the a priori tuning. Results show that assimilation of the satellite data improves model representation of major circulation features, which translate into improved three-dimensional distributions of temperature and salinity. The multivariate assimilation also improves the agreement of subsurface nitrate through its tight correlation with temperature, but the improvements in subsurface chlorophyll were modest initially due to suboptimal choices of the model’s optical module. Repeating the assimilation run after adjusting light attenuation parameterization through further a priori tuning greatly improved the subsurface distribution of chlorophyll. Therefore, even sparse BGC-Argo observations can provide substantial benefits to biogeochemical prediction by enabling a priori model tuning. Given that, so far, the abundance of BGC-Argo profiles in the Gulf of Mexico and elsewhere is insufficient for sequential assimilation, updating 3D biological properties in a model that has been well calibrated is an intermediate step toward full assimilation of the new data types.

scholarly journals Seasonal patterns in phytoplankton biomass across the northern and deep Gulf of Mexico: a numerical model study

2018 ◽  
Vol 15 (11) ◽  
pp. 3561-3576 ◽  
Author(s):  
Fabian A. Gomez ◽  
Sang-Ki Lee ◽  
Yanyun Liu ◽  
Frank J. Hernandez Jr. ◽  
Frank E. Muller-Karger ◽  
...  
Keyword(s):  
Gulf Of Mexico ◽  
Primary Production ◽  
Phytoplankton Biomass ◽  
Mississippi Delta ◽  
Three Dimensional ◽  
Rate Of Change ◽  
Integrated Analysis ◽  
Biogeochemical Model ◽  
Seasonal Patterns ◽  
Biogeochemical Models

Abstract. Biogeochemical models that simulate realistic lower-trophic-level dynamics, including the representation of main phytoplankton and zooplankton functional groups, are valuable tools for improving our understanding of natural and anthropogenic disturbances in marine ecosystems. Previous three-dimensional biogeochemical modeling studies in the northern and deep Gulf of Mexico (GoM) have used only one phytoplankton and one zooplankton type. To advance our modeling capability of the GoM ecosystem and to investigate the dominant spatial and seasonal patterns of phytoplankton biomass, we configured a 13-component biogeochemical model that explicitly represents nanophytoplankton, diatoms, micro-, and mesozooplankton. Our model outputs compare reasonably well with observed patterns in chlorophyll, primary production, and nutrients over the Louisiana–Texas shelf and deep GoM region. Our model suggests silica limitation of diatom growth in the deep GoM during winter and near the Mississippi delta during spring. Model nanophytoplankton growth is weakly nutrient limited in the Mississippi delta year-round and strongly nutrient limited in the deep GoM during summer. Our examination of primary production and net phytoplankton growth from the model indicates that the biomass losses, mainly due to zooplankton grazing, play an important role in modulating the simulated seasonal biomass patterns of nanophytoplankton and diatoms. Our analysis further shows that the dominant physical process influencing the local rate of change of model phytoplankton is horizontal advection in the northern shelf and vertical mixing in the deep GoM. This study highlights the need for an integrated analysis of biologically and physically driven biomass fluxes to better understand phytoplankton biomass phenologies in the GoM.

scholarly journals Water resource monitoring systems and the role of satellite observations

2011 ◽  
Vol 15 (1) ◽  
pp. 39-55 ◽  
Author(s):  
A. I. J. M. van Dijk ◽  
L. J. Renzullo
Keyword(s):  
Remote Sensing ◽  
Data Assimilation ◽  
Water Use ◽  
Water Resource ◽  
Model Evaluation ◽  
A Priori ◽  
Satellite Observations ◽  
Monitoring Systems ◽  
Resource Monitoring ◽  
Operational Systems

Abstract. Spatial water resource monitoring systems (SWRMS) can provide valuable information in support of water management, but current operational systems are few and provide only a subset of the information required. Necessary innovations include the explicit description of water redistribution and water use from river and groundwater systems, achieving greater spatial detail (particularly in key features such as irrigated areas and wetlands), and improving accuracy as assessed against hydrometric observations, as well as assimilating those observations. The Australian water resources assessment (AWRA) system aims to achieve this by coupling landscape models with models describing surface water and groundwater dynamics and water use. A review of operational and research applications demonstrates that satellite observations can improve accuracy and spatial detail in hydrological model estimation. All operational systems use dynamic forcing, land cover classifications and a priori parameterisation of vegetation dynamics that are partially or wholly derived from remote sensing. Satellite observations are used to varying degrees in model evaluation and data assimilation. The utility of satellite observations through data assimilation can vary as a function of dominant hydrological processes. Opportunities for improvement are identified, including the development of more accurate and higher spatial and temporal resolution precipitation products, and the use of a greater range of remote sensing products in a priori model parameter estimation, model evaluation and data assimilation. Operational challenges include the continuity of research satellite missions and data services, and the need to find computationally-efficient data assimilation techniques. The successful use of observations critically depends on the availability of detailed information on observational error and understanding of the relationship between remotely-sensed and model variables, as affected by conceptual discrepancies and spatial and temporal scaling.

scholarly journals Seasonal Patterns in Phytoplankton Biomass across the Northern and Deep Gulf of Mexico: A Numerical Model Study

2017 ◽  
Author(s):  
Fabian A. Gomez ◽  
Sang-Ki Lee ◽  
Yanyun Liu ◽  
Frank J. Hernandez Jr. ◽  
Frank E. Muller-Karger ◽  
...  
Keyword(s):  
Gulf Of Mexico ◽  
Phytoplankton Biomass ◽  
Three Dimensional ◽  
Trophic Levels ◽  
Biogeochemical Model ◽  
Seasonal Patterns ◽  
Biogeochemical Models ◽  
Model Study ◽  
Numerical Model Study

Abstract. Biogeochemical models that simulate realistic lower trophic levels dynamics, including the representation of main phytoplankton and zooplankton functional groups, are valuable tools for our understanding of natural and anthropogenic disturbances in marine ecosystems. However, previous three-dimensional biogeochemical modeling studies in the northern and deep Gulf of Mexico (GoM) have used only one phytoplankton and one zooplankton type. To advance our modeling capability of the GoM ecosystem and to investigate the dominant spatial and seasonal patterns phytoplankton biomass, we configured a 14-component biogeochemical model that explicitly represents nanophytoplankton, diatoms, micro-, and mesozooplankton. Our model outputs compare well with satellite and in situ observations, reproducing dominant seasonal patterns in chlorophyll and primary production. The model results show that diatom growth is strongly silica limited (> 95 %) in the deep GoM, and both nitrogen and silica limited (30–70 %) in the northern shelf. Nanophytoplankton growth is weakly nutrient limited in the Mississippi delta year-round (

scholarly journals Water resource monitoring systems and the role of satellite observations

2010 ◽  
Vol 7 (4) ◽  
pp. 6305-6349 ◽  
Author(s):  
A. I. J. M. van Dijk ◽  
L. J. Renzullo
Keyword(s):  
Remote Sensing ◽  
Data Assimilation ◽  
Water Use ◽  
Water Resource ◽  
Model Evaluation ◽  
A Priori ◽  
Satellite Observations ◽  
Monitoring Systems ◽  
Resource Monitoring ◽  
Operational Systems

Abstract. Spatial water resource monitoring systems (SWRMS) can provide valuable information in support of water management, but current operational systems are few and provide only a subset of the information required. Necessary innovations include the explicit description of water redistribution and water use from river and groundwater systems, achieving greater spatial detail (particularly in key features such as irrigated areas and wetlands), and improving accuracy as assessed against hydrometric observations, as well as assimilating those observations. The Australian water resources assessment (AWRA) system aims to achieve this by coupling landscape models with models describing surface water and groundwater dynamics and water use. A review of operational and research applications demonstrates that satellite observations can improve accuracy and spatial detail in hydrological model estimation. All operational systems use dynamic forcing, land cover classifications and a priori parameterisation of vegetation dynamics that are partially or wholly derived from remote sensing. Satellite observations are used to varying degrees in model evaluation and data assimilation. The utility of satellite observations through data assimilation can vary as a function of dominant hydrological processes. Opportunities for improvement are identified, including the development of more accurate and higher spatial and temporal resolution precipitation products, and the use of a greater range of remote sensing products in a priori model parameter estimation, model evaluation and data assimilation. Operational challenges include the continuity of research satellite missions and data services, and the need to find computationally-efficient data assimilation techniques. The successful use of observations critically depends on the availability of detailed information on observational error and understanding of the relationship between remotely-sensed and model variables, as affected by conceptual discrepancies and spatial and temporal scaling.

scholarly journals Data assimilation of Argo profiles in a northwestern Pacific model

2017 ◽  
Vol 17 (1) ◽  
pp. 17-30 ◽  
Author(s):  
Zhaoyi Wang ◽  
Andrea Storto ◽  
Nadia Pinardi ◽  
Guimei Liu ◽  
Hui Wang
Keyword(s):  
Data Assimilation ◽  
Positive Impact ◽  
Three Dimensional ◽  
Predictive Performance ◽  
Ocean Model ◽  
Northwestern Pacific ◽  
Background Error ◽  
Salinity Data ◽  
Argo Profiles ◽  
Assimilation Scheme

Abstract. Based on a novel estimation of background-error covariances for assimilating Argo profiles, an oceanographic three-dimensional variational (3DVAR) data assimilation scheme was developed for the northwestern Pacific Ocean model (NwPM) for potential use in operational predictions and maritime safety applications. Temperature and salinity data extracted from Argo profiles from January to December 2010 were assimilated into the NwPM. The results show that the average daily temperature (salinity) root mean square error (RMSE) decreased from 0.99 °C (0.10 psu) to 0.62 °C (0.07 psu) in assimilation experiments throughout the northwestern Pacific, which represents a 37.2 % (27.6 %) reduction in the error. The temperature (salinity) RMSE decreased by  ∼  0.60 °C ( ∼  0.05 psu) for the upper 900 m (1000 m). Sea level, temperature and salinity were in better agreement with in situ and satellite datasets after data assimilation than before. In addition, a 1-month experiment with daily analysis cycles and 5-day forecasts explored the performance of the system in an operational configuration. The results highlighted the positive impact of the 3DVAR initialization at all forecast ranges compared to the non-assimilative experiment. Therefore, the 3DVAR scheme proposed here, coupled to ROMS, shows a good predictive performance and can be used as an assimilation scheme for operational forecasting.

scholarly journals Application of Data Assimilation and the Relationship between ENSO and Precipitation

2021 ◽  
Vol 26 (1) ◽  
pp. 24
Author(s):  
Sittisak Injan ◽  
Angkool Wangwongchai ◽  
Usa Humphries
Keyword(s):  
Data Assimilation ◽  
Correlation Coefficient ◽  
Southern Oscillation ◽  
Three Dimensional ◽  
Coupled Model ◽  
Heavy Precipitation ◽  
Sea Surface ◽  
Intermediate Coupled Model ◽  
The Relationship ◽  
Better Than

Climate change in Thailand is related to the El Niño and Southern Oscillation (ENSO) phenomenon, in particular drought and heavy precipitation. The data assimilation method is used to improve the accuracy of the Ensemble Intermediate Coupled Model (EICM) that simulates the sea surface temperature (SST). The four-dimensional variational (4D-Var) and three-dimensional variational (3D-Var) schemes have been used for data assimilation purposes. The simulation was performed by the model with and without data assimilation from satellite data in 2011. The result shows that the model with data assimilation is better than the model without data assimilation. The 4D-Var scheme is the best method, with a Root Mean Square Error (RMSE) of 0.492 and a Correlation Coefficient of 0.684. The relationship between precipitation in Thailand and the ENSO area in Niño 3.4 was consistent for seven months, with a correlation coefficient of −0.882.

scholarly journals A three-dimensional variational data assimilation system for aerosol optical properties based on WRF-Chem: design, development, and application of assimilating Himawari-8 aerosol observations

2021 ◽  
Author(s):  
Daichun Wang ◽  
Wei You ◽  
Zengliang Zang ◽  
Xiaobin Pan ◽  
Yiwen Hu ◽  
...  
Keyword(s):  
Air Pollution ◽  
Optical Properties ◽  
Data Assimilation ◽  
North China ◽  
Model Simulation ◽  
Three Dimensional ◽  
Mie Scattering ◽  
State Variables ◽  
Aerosol Optical Properties ◽  
Optical Module

Abstract. This paper presents a three-dimensional variational (3DVAR) data assimilation (DA) system for aerosol optical properties, including aerosol optical depth (AOD) retrievals and lidar-based aerosol profiles, which was developed for the Model for Simulating Aerosol Interactions and Chemistry (MOSAIC) within the Weather Research and Forecasting model coupled to Chemistry (WRF-Chem) model. For computational efficiency, 32 model variables in the MOSAIC_4bin scheme are lumped into 20 aerosol state variables that are representative of mass concentrations in the DA system. To directly assimilate aerosol optical properties, an observation operator based on the Mie scattering theory was employed, which was obtained by simplifying the optical module in WRF-Chem. The tangent linear (TL) and adjoint (AD) operators were then established and passed the TL/AD sensitivity test. The Himawari-8 derived aerosol optical thickness (AOT) data were assimilated to validate the system and investigate the effects of assimilation on both AOT and PM2.5 simulations. Two comparative experiments were performed with a cycle of 24 h from November 23 to 29, 2018, during which a heavy air pollution event occurred in North China. The DA performances of the model simulation were evaluated against independent aerosol observations, including the Aerosol Robotic Network (AERONET) AOT and surface PM2.5 measurements. The results show that Himawari-8 AOT assimilation can significantly improve model AOT analyses and forecasts. Generally, the control experiments without assimilation seriously underestimated AOTs compared with observed values and were therefore unable to describe real aerosol pollution. The analysis fields closer to observations improved AOT simulations, indicating that the system successfully assimilated AOT observations into the model. In terms of statistical metrics, assimilating Himawari-8 AOTs only limitedly improved PM2.5 analyses in the inner simulation domain (D02); however, the positive effect can last for over 24 h. Assimilation effectively enlarged the underestimated PM2.5 concentrations to be closer to the real distribution in North China, which is of great value for studying heavy air pollution events

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